Clinical and Investigative Medicine最新文献

Purpose: To investigate the metabolic profile and biomarkers of schizophrenia with auditory hallucinations (AHs).

Methods: A total of 18 schizophrenic patients with the symptom of pure AHs (pAHs), 28 without AH (nAHs) and 43 age-matched healthy persons (Con) were enrolled in this study. Participants in pAHs and nAHs groups had relapsed into exacerbations of psychosis after self-discontinuing antipsychotics for at least one month; blood samples were drawn prior to restarting anti-psychotic treatment. Participants with history of recreational substance use were excluded. Positive and Negative Syndrome Scale (PANSS) and Auditory Hallucinations Rating Scale (AHRS) were used to assess the clinical mental state of all samples. Enzyme-linked immunosorbent assay (ELISA) was used to estimate the level of cytokines, and metabolomics analysis to identify potential biomarkers and pathways in the three groups. Graphpad 8.0 software was used to calculate the area under the receiver operating characteristic (ROC) curve. The relationship between metabolites and cytokines were determined using correlation analysis.

Results: Questionnaire scores showed significant differences in the positive symptom scale and PANSS total between nAHs and pAHs groups. Four cytokines (BDNF, IL-2, NGF-β and TNF-α) differed significantly among the three groups. Six molecules in the nAHs group (phenylalanine, hippurate, serine, glutamate, valine and cystine) and four in the pAHs group (phenylalanine, serine, glutamate and cystine) were identified as potential biomarkers. In addition, phenylalanine was shown as a potential independent diagnostic biomarker for pAHs. Correlation analysis revealed that cystine and serine were significantly negatively correlated with IL-2 in the pAHs group.

Conclusions: This study revealed the metabolic profile of patients with schizophrenia with AHs and provided new information to support the diagnosis. The identification of unique biomarkers would contribute to objective and reliable diagnoses of patients with schizophrenia with AH.

Purpose: The purpose of this scoping review was to identify existing clinical and basic science knowledge surrounding the effect of muscarinic receptor antagonism on allergen-induced airway responses to inform future clinical research in this area.

Methods: Multiple advanced searches were performed using the National Library of Medicine PubMed search engine. Each search began with two terms; for example, "atropine and asthma" or "tiotropium and airway inflammation". Results were then further refined to include terms such as "allergen" or "ovalbumin (OVA)". Abstracts of refined searches were reviewed for relevance to allergic asthma and allergen-induced airway responses including the early and late asthmatic responses, airway inflammation and tissue remodelling. There was no restriction regarding publication date. Reference lists of selected papers were also reviewed for relevant publications.

Results: Nine human clinical trial publications and fourteen animal model publications were identified. In humans, single dose atropine (n=4), ipratropium (n=4) or oxitropium (n=1) administered pre-challenge produced equivocal effects on allergen-induced early asthmatic responses as reported but favored inhibition in eight of nine studies after re-analyses. Animal model investigations (n=14) showed mostly favorable results, especially with respect to airway inflammation and tissue remodelling, although two studies were negative, and one study showed a worsening in allergen induced airway inflammation following muscarinic receptor antagonism.

Conclusion: Existing human and animal model data suggest muscarinic receptor antagonism may be beneficial in preventing allergen induced airway responses in those with allergic asthma. Additional human research utilizing current standardized methodologies is required.

Purpose: Through comprehensive bioinformatics analysis based on the immune microenvironment, this study aimed to identify immune-related RNA biomarkers that indicate aneurysmal subarachnoid hemorrhage (aSAH).

Methods: The GSE73378 dataset was downloaded from the National Center for Biotechnology Information GEO database, providing blood from 107 normal controls and 103 patients with aSAH. The immune infiltration types in the aSAH blood samples were assessed and RNAs that were differentially expressed (DE) between 1) the aSAH and control groups and 2) the immune infiltration groups (high and low) were identified. The intersecting genes were subjected to weighted gene co-expression network analysis followed by co-expression network construction. The aSAH-related genes and pathways were identified from the Comparative Toxicogenomics Database: update 2019.

Results: A total of three DE long non-coding RNAs (lncRNAs) and 301 DE mRNAs were identified. Of the 301 mRNAs, 91 were significantly enriched in three modules. Based on the 91 mRNAs and three lncRNAs, a co-expression network related to the disease pathway was constructed. This pathway consisted of 16 factors, including the 13 mRNAs (e.g., TNFSF13B, TNFSF10, MYD88, GNA12 and NSMAF) and three lncRNAs (FAM66A, LINC00954 and CELF2-AS2), as well as six pathways, including the NF-κB, toll-like receptor, and sphingolipid signalling pathways.

Conclusion: TNFSF13B, MYD88, GNA12, NSMAF, FAM66A, LINC00954 and CELF2-AS2 may serve as biomarkers for aSAH. The NF-κB, toll-like receptor and sphingolipid signalling pathways may play critical roles in the progression of aSAH.

Purpose: Disease prevalence estimates from population-based administrative databases are often biased due to measurement (misclassification) errors. The purpose of this article is to review the methodology for estimating disease prevalence in administrative data, with a focus on bias correction.

Source: Several approaches to bias correction in administrative data were reviewed and application of these methods was demonstrated using an example from the literature: physician claims and hospitalization data were employed to estimate diabetes prevalence in Ontario, Canada.

Findings: Misclassification bias in prevalence estimates from administrative data can be reduced by developing and selecting an optimal algorithm for case identification, applying a bias correction formula, or using statistical modelling. An algorithm for which sensitivity equals positive predictive value provides an unbiased estimate of prevalence. Bias reduction methods generally require information about the measurement properties of the algorithm, such as sensitivity, specificity, or predictive value. These properties depend on disease type, prevalence, algorithm definition (including the observation window), and may vary by population and time. Prevalence estimates can be improved by applying multivariable disease prediction models.

Conclusion: Frequency of a positive case identification algorithm in administrative data is generally not equivalent to disease prevalence. Although prevalence estimates can be corrected for bias using known measurement properties of the algorithm, these properties may be difficult to estimate accurately; therefore, disease prevalence estimates based on administrative data must be treated with caution.